Abstract
High precision localization information is the precondition of unmanned ground vehicles. But the global navigation satellite system (GNSS) signal turns unreliable in urban and forest areas since it is blocked by buildings and trees easily, which causes decline of localization accuracy. In order to solve this problem, an integrated navigation system based on the strapdown inertial navigation system and binocular camera visual odometer is utilized in this paper to provide navigation parameters for unmanned ground vehicles when the GNSS signal denies. However, the existing integrated navigation algorithm cannot meet the requirement of the high precision localization for unmanned ground vehicles because of the uncertainty and nonlinearity. As a result, a robust nonlinear filter based on the $H_\infty $ filter and the cubature Kalman filter, named RHCKF, is proposed in this paper, adopted in unmanned vehicle navigation. Simulation and real test are both carried out to verify the effectiveness of the novel navigation algorithm when the GNSS signal denies.
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